Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

ACS ES&T Water, Год журнала: 2024, Номер 4(6), С. 2619 - 2631

Опубликована: Май 1, 2024

Land area optimization for horizontal flow constructed wetlands (HFCWs) with a low organic loading rate (OLR) needs special considerations as the microflora changes dramatically OLR. The P-k-C* approach does not lead to an accurate calculation of k-values in these wetlands. In this research, nonlinear machine learning models [Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN)] are applied predict realistic k-values. Data from 37 low-OLR HFCWs (n = 544) were analyzed, calculated found vary markedly (0.059–0.249 average 0.113 ± 0.090 m/day). classification based on OLR, rate, media depth leads reduction standard deviations (SDs) 83.40 35.27%. least SDs needed optimal design CWs. SVR, RF, ANN tested, best prediction efficiency testing datasets was achieved through model R2(kTKN)= 0.768 (RMSE 0.067) total Kjeldahl nitrogen (TKN), R2(kTN)= 0.835 0.043) (TN), R2(kTP) 0.723 0.087) phosphorus (TP). outcome validated using primary data HFCWs, which also confirmed superiority ANN-based model, can be used customization HFCWs.

Язык: Английский

---

A Futuristic Approach to Subsurface-Constructed Wetland Design for the South-East Asian Region Using Machine Learning

ACS ES&T Water, Год журнала: 2024, Номер 4(9), С. 4061 - 4074

Опубликована: Авг. 29, 2024

This study investigates the optimized design of horizontal flow constructed wetlands (HFCWs) to enhance pollutant removal efficiency while minimizing surface area requirements, particularly in Southeast Asian region. By refining first-order rate coefficient (k) for organics and nutrients, research aims meet specific performance benchmarks across three scenarios, ensuring compliance with discharge or reuse standards. Utilizing a data set comprising 1680 entries, five machine learning models─multiple linear regression (MLR), eXtreme Gradient Boosting (XGBoost), random forest (RF), artificial neural network (ANN), support vector (SVR)─were employed predict k values. Pearson's correlation, heat maps, ANOVA analysis identified most influential parameters affecting k-value predictions. The values ranged from 0.01 0.52 per day using P–k–C* method, essential effective removal. SVR model demonstrated highest predictive accuracy, R2 0.91 kBOD, 0.90 kTN, 0.82 kTKN, 0.76 kTP. optimization reduced standard deviations significantly, 136.90% 2.28%. Consequently, required wetland was by up 68% biochemical oxygen demand (BOD), 60% TN (total nitrogen), 67% TP phosphorus) larger systems, supporting tailored HFCWs targeted

Язык: Английский

---

Alum sludge-driven electro-phytoremediation in constructed wetlands: a novel approach for sustainable nutrient removal

RSC Advances, Год журнала: 2025, Номер 15(4), С. 2947 - 2957

Опубликована: Янв. 1, 2025

In addition to their advantages as promising methods for wastewater treatment, CWs exhibit poor performance in terms of N and P removal efficiency the effluent treatment plants. By focusing on this issue, we designed integrated with a biochar-doped activated carbon cloth (ACC) electrode alum sludge from water plants substrate achieve concomitant organic matter nutrient efficiency. Compared use one layer (CWs-C3) ACC electrodes inserted two layers, which uses sludge, significant improvement was achieved (96% COD; 89% TN; 77% TP). The findings revealed that application potential accompanied by insertion cathode into first beneficial completing nitrification facilitating denitrification anode regions, respectively, resulting increased nutrients. Further evaluation TN-TP synergetic mechanism influenced Fe2+ an electron donor driving force development autotrophic denitrifying bacteria increase nitrate reduction. Additionally, formation FePO4 AlPO4 adsorption through interaction FeOOH AlOOH phosphate constitute main TP wastewater. Another reason CW-C3 reactor greater abundance microbial diversity effectuated regions. summary, strategy simultaneously promoting nutrients utilizing large scale practical applications proposed.

Язык: Английский

---

The crucial factor for microplastics removal in large-scale subsurface-flow constructed wetlands

Journal of Hazardous Materials, Год журнала: 2024, Номер 480, С. 136023 - 136023

Опубликована: Окт. 1, 2024

Язык: Английский

---

New insights into biofilm formation and microbial communities in hybrid constructed wetlands with functional substrates for treating contaminated surface water

Bioresource Technology, Год журнала: 2024, Номер 416, С. 131741 - 131741

Опубликована: Ноя. 2, 2024

Язык: Английский

---

New Perspective for the Prediction of Pollutant Removal Efficiency in Constructed Wetlands: Using a Genetic Algorithm-Assisted Machine Learning Model

ACS ES&T Water, Год журнала: 2024, Номер 4(11), С. 5053 - 5064

Опубликована: Окт. 24, 2024

Constructed wetlands (CWs) are widely used for wastewater treatment, but their performance is difficult to predict due varying factors like local weather, hydraulic conditions, vegetation, and composition. Here, we proposed a model method predicting CW processing efficiency based on published literature simulations using machine learning methods. Through data mining, divided the obtained variables into six different categories repair strategies each category. To improve performance, genetic algorithm-assisted database dimensionality reduction was introduced in destruction. After selection hyperparameter optimization, random forest algorithm selected as final algorithm, performances all four predictions (ammonia nitrogen, total phosphorus, chemical oxygen demand removal efficiency) were 0.9405, 0.8277, 0.8136, 0.8877, respectively. Generally, magnitude of influence listed following order: meteorology/location > condition substrate property ≈ water quality reactor size vegetation. Based this work, future design operation CWs might find an efficient environmentally friendly that could ideally maximize pollution control economic benefits at same time.

Язык: Английский

---

Microalgae Shed Light on Interconnected Nitrogen Transformation in Microalgal-Bacterial Consortia

ACS ES&T Water, Год журнала: 2024, Номер 4(8), С. 3131 - 3144

Опубликована: Июль 31, 2024

Nitrogen is a component of many fundamental biomolecules and also participates in environmental redox chemistry. pollution serious problem. Biological nitrogen removal one the most significant issues wastewater treatment. Microbial-driven transformations are carried out through metabolic pathways. Wastewater treatment systems using microalgal-bacterial cocultures can improve nutrient efficiency interspecies synergistic interactions. However, relevant studies on metabolism microbial characteristics have not been systematically reviewed discussed. This Review comprehensively analyzes contaminants various biological composite summarizes nitrogen-metabolizing enzymes present. regulation methods involving quorum sensing genetic described, enhancing community interactions, enzyme activity, promoting electron transfer introduced detail. provides perspective improvement optimization technology analyzing increasing process performance.

Язык: Английский

---

Harnessing the rhizosphere sponge to smooth pH fluctuations and stabilize contaminant retention in biofiltration system

Bioresource Technology, Год журнала: 2024, Номер 418, С. 131971 - 131971

Опубликована: Дек. 12, 2024

Язык: Английский

---